For improving the performance of wireless communication networks or radio systems, multi antenna techniques using group antennas (antenna arrays) at the transmitting side and at the receiving side may be used. One approach is called beamforming, and in accordance with this approach a signal is split at the transmitter and multiplied by a complex weighting factor (having a magnitude and a phase) for every transmitter antenna individually. At the receiver, the signals of the individual receiving antennas are also weighted with complex factors and added. Weighting the signals of a group antenna is implemented by a beamformer. If the weights all have constant amplitude and differ only in phase, this is referred to as equal-gain beamforming or as a phased array. Contrary to the beamforming signal processing, in MIMO signal processing (MIMO=Multiple-Input Multiple-Output), not only complex weightings but also costly digital signal processing operations need to be performed in every branch. The MIMO operations may each have a different effect on certain portions of the antenna signals (samples in time or frequency), whereas in beamforming all signal portions are weighted identically. Equal-gain beamformers may be implemented in analog circuitry with relatively little effort and are hence particularly interesting when a large number of antennas is used. In contrast, systems using MIMO signal processing entail a higher effort in the analog and digital circuitry and are hence generally limited to moderate numbers of antennas, e.g. to only 2 or 4 antennas.
FIG. 1 shows a schematic equivalent baseband representation of a unidirectional wireless communication system comprising M antennas at the transmitter and N antennas at the receiver. The system 100 comprises a transmitter 102 having an input 104 at which an input data signal ds to be transmitted in the wireless communication system or radio system 100 is received. The transmitter comprises a plurality of antennas 1051, 1052, . . . 105M, i.e. the transmitter 102 comprises M antennas. The input data signal received at the input 104 is processed by a transmitter signal processing unit 106 which outputs a signal x to be transmitted. The signal x received at the beamformer input 107 is distributed via a transmit beamformer 108 to the respective antennas 1051 to 105M. The beamformer 108 comprises a dividing or splitting circuit 109 and a plurality of weighting elements 1101, 1102, . . . 110M applying to the input signal x received at the beamformer input 107 respective weighting factors w1, w2, . . . , wM. The weighted input signals are transmitted from the antennas 1051 to 105M via a radio channel 112 to a receiver 114. The receiver 114 comprises a plurality of receive antennas 1161, 1162, . . . , 116N. The signals received from the respective antennas 1161 to 116N are fed into a receive beamformer 118. The receive beamformer 118 comprises a plurality of weighting elements 1201, 1202, . . . 120N that are provided for applying to the respective signals received from the antennas 1161 to 116N the respective weighting factors z1, z2, . . . zN and an adding circuit 122. The adding circuit adds the weighted receive signals to form the output signal y of the beamformer 118 that is provided at an output 124. The signal y is fed into the receiver signal processing unit 126 providing the received data signal dr at the output 128 of the receiver 114. In case beamforming is done at the transmitter and at the receiver, a beamforming system comprises a transmit beamformer, transmit antennas, receive antennas and a receive beamformer. For example, the transmit beamformer 108, the transmit antennas 1051 to 105M, the receive antennas 1161 . . . 116N and the receive beamformer 118 shown in FIG. 1 form a beamforming system. When beamforming is only applied at the transmitter, the beamforming system comprises the transmit beamformer, the transmit antennas, and the receive antennas. Alternatively, when using beamforming only at the receiver, the beamforming system comprises the transmit antennas, the receive antennas, and the receive beamformer.
At the transmitter 102 M beamforming branches are formed, each of the beamforming branches comprises one of the weighting elements of the beamformer 108 and one of the antennas of the transmitter. For example, a first beamforming branch is formed by the weighting element 1101 of the beamformer 108 and the antenna 1051. Likewise, at the receiver 114 N beamforming branches are formed, the respective branches comprises one of the weighting elements of the beamformer 118 and one of the antenna elements of the receiver. For example, a first beamforming branch at the receiver 114 is formed by the antenna element 1161 and the weighting element 1201 of the receive beamformer 118.
By beamforming at the transmitter 102, the power radiated in certain space directions is increased, while it is reduced in other space directions. Beamforming at the receiver 114 has the effect that signals from certain space directions are received in an amplified manner and from other space directions in an attenuated manner. Because the transmission attenuation increases with rising transmission frequencies, beamforming is considered as promising and inexpensive means for increasing the performance of systems having high transmission frequencies, e.g. future 60 GHz systems.
The weighting factors w1, w2, . . . , wM or z1, z2, . . . , zM for the individual antennas 1051 to 105M or 1161 to 116N at the transmitter 102 or at the receiver 114 may each be combined into one beamforming vector. FIG. 1 shows an example of an unidirectional wireless communication system allowing for a transmission using M beamforming branches at the transmitter 102 and N beamforming branches at the receiver 114. The adjustment of the signals provided by the transmitter 102 using the transmit beamformer 108 is described by the transmit beamforming vector w:
  w  =      (                                        w            1                                                            w            2                                                ⋮                                                  w            M                                )  
The adjustment of the signals received at the receiver 114 using the receive beamformer 118 is described by the receive beamforming vector z:
  z  =      (                                        z            1                                                            z            2                                                ⋮                                                  z            N                                )  
In the case of using the equal-gain beamforming, the elements of the beamforming vectors have a constant modulus. If the magnitude of the beamforming vectors is defined to 1, the beamforming vectors are given as follows:
      w    =                            1          M                    ⁢              (                                                            exp                ⁡                                  (                                      jϑ                    1                                    )                                                                                                        exp                ⁡                                  (                                      jϑ                    2                                    )                                                                                        ⋮                                                                          exp                ⁡                                  (                                      jϑ                    M                                    )                                                                    )              and      z    =                            1          N                    ⁢              (                                                            exp                ⁡                                  (                                      jφ                    1                                    )                                                                                                        exp                ⁡                                  (                                      jφ                    2                                    )                                                                                        ⋮                                                                          exp                ⁡                                  (                                      jφ                    N                                    )                                                                    )            whereinθm=phase values it θmε[0, 2π] for the transmitter 102, andφn=phase values φnε[0, 2π] for the receiver 114.
Many known systems may use discrete (quantized) phase values only, so that the number of possible beamforming vectors is limited.
The wireless transmission between the antenna groups 106 and 116 at the transmitting side 102 and at the receiving side 114 is performed via the radio channel 112 including all possible connection paths between all transmitting antennas 1061 to 106M and all receiving antennas 1161 to 116N. The radio channel 112 is defined using a matrix, the so called channel matrix H.
The presented beamforming techniques are considered for a unidirectional transmission between a transmitter 102 and a receiver 114. Conventionally, wireless communications systems are provided for a bidirectional transmission between stations. Each station needs to be provided with a transmitter and a receiver. Both in the transmitter and in the receiver beamforming techniques may be used. FIG. 1(a) depicts a bidirectional, wireless beamforming transmission system 900 having two stations 902 and 904. Each station is provided with a transmitter 906, 910 and a receiver 908, 912 having a structure as described in FIG. 1. Up to four beamforming vectors may be involved in case of such a bidirectional transmission between the two stations, station 902 and station 904: for a transmission from the station 902 to the station 904 the beamformer 914 at the station 902 may use for a transmitting beamforming at station 902, and the beamformer 916 at the station 904 may use for a receiving beamforming at station 904; and for a transmission from the station 904 to the station 902 the beamformer 918 at the station 904 may use for a transmitting beamforming at station 904, and the beamformer 920 at the station 902 may use for a receiving beamforming at station 902. Since a bidirectional transmission can be split into two unidirectional transmissions in opposite directions, with respect to the beamforming techniques it is sufficient to consider a unidirectional transmission and a unidirectional transmission system, respectively, including one transmitter and one receiver.
A problem for the operation of a multi-antenna system using beamforming is the adaptive (dynamic) adjustment of the beamforming vectors for maximizing the transmission quality in dependence on the propagation conditions. The methods for determining beamforming vectors may be divided into two categories: Methods with explicit beamforming channel knowledge, and methods without beamforming channel knowledge. In the former case, beamforming channel knowledge means that the radio channel 112 between any transmitting beamformer antenna element 1061 to 106M and any receiving beamformer antenna element 1161 to 116N, i.e. the beamforming channel matrix, is known. In the latter case, estimating the channel matrix presents a significant additional challenge. In bidirectional transmission, in general, two channel matrices are to be considered: one for the forward direction and one for the backward direction, and they have to be acquired in practice by a beamforming channel estimation.
The following problems occur when using a beamforming system with beamforming signal processing according to FIG. 1:    1. Determining optimal beamforming vectors at transmitter and receiver without explicit channel knowledge.    2. Estimating a multi-antenna channel in systems with beamforming signal processing.    3. Determining suitable beamforming vectors at the transmitter and at the receiver using channel knowledge for systems with pure beamforming signal processing.The following problem occurs when using a hybrid MIMO beamforming system with MIMO signal processing and beamforming signal processing according to FIG. 5:    4. Determining suitable beamforming vectors at the transmitter and at the receiver in hybrid MIMO beamforming systems.
For determining suitable beamforming vectors, known methods without explicit channel knowledge provide for a training phase, during which test signals or training symbols are transmitted and evaluated within a training frame at different suitably selected beamforming vectors (see e.g. ECMA-387 Standard: High Rate 60 GHz PHY, MAC and HDMI PAL, 2008, Ecma International). The temporal sequence of beamforming adjustments may be described by a matrix (a training matrix), which consists of the respective beamforming vectors. In a bidirectional radio system using two-way beamforming in the transmitting and receiving branches, transmission of training frames is performed in both directions. Optimizing the beamforming vectors is obtained by repeating the alternating transmission several times and iteratively adapting the beamforming vectors.
At present methods for determining the beamforming channel matrix are only known for systems where a group antenna is used only on one side (at the transmitter or at the receiver). In such a case, the beamforming channel matrix transitions into a beamforming channel vector, which is calculated using side information. The side information relate to the direction of incidence of the receive signal or the desired transmitting direction of the transmit signal and the geometry of the group antenna. This involves the presence of definite a-priori directional information and only little multipath propagation may exist in the radio channel (a typical field of such an application is the communication to a geostationary satellite, a communication from a vehicle, or a target tracking radar). Estimating the directional information for the receiver merely from the receive signals without a-priori information is possible, involves, however, MIMO signal processing see e.g. Chung, Pei-Jung and Bohme, J. F., “Recursive EM and SAGE-inspired algorithms with application to DOA estimation” Signal Processing, IEEE Transactions on, 53(8):2664-2677, 2005; Schmidt, R., “Multiple emitter location and signal parameter estimation”, Antennas and Propagation, IEEE Transactions on, 34(3):276-280, 1986; or Stoica, P. and Sharman, K. C., “Maximum likelihood methods for direction-of-arrival estimation”, Acoustics, Speech and Signal Processing, IEEE Transactions on, 38(7):1132-1143, 1990).
Methods for determining a beamforming vector on the transmitter side or on the receiver side using channel knowledge from the directional information have been known for a long time for phased-array applications. However, these direction-based methods may only be applied with little or non-existing multipath propagation. Methods for determining the optimal beamforming vectors on the transmitter side and on the receiver side using channel knowledge—also with multipath propagation—have so far only been known for systems having MIMO signal processing (see e.g. Heath, R. W., Jr. and Paulraj, A., “Multiple antenna arrays for transmitter diversity and space-time coding”, Communications, 1999. ICC '99. 1999 IEEE International Conference on, pages 36-40 vol. 1., 1999). For MIMO systems, different approaches for determining the channel matrix are known. Transferring such techniques to systems having only beamforming signal processing has not been possible so far, since, on the one hand, channel knowledge without side information (directional information) was not available for these systems and, on the other hand, it was unclear how a common beamforming vector is to be determined for all possibly different signal portions (in time and frequency).
For hybrid methods the principle of combining beamforming and MIMO signal processing is described e.g. by Dammann, A. and Raulefs, R. and Kaiser, S., “Beamforming in combination with space-time diversity for broadband OFDM systems”, Communications, 2002. ICC 2002. IEEE International Conference on, pages 165-171, 2002. Smart antennas are controlled via an adaptive antenna processor. The aim of beamforming is the transmission of the signal via several ideally statistically independent propagation paths. On the transmitting side, the data stream is split into several sub-streams based on the diversity principle, and combined again on the receiving side. Among others, space-time coding (STC) as a form of MIMO signal processing is suggested as method. Further, when using beamforming at the transmitter and receiver, a mutual allocation of the transmitting and the receiving antenna groups may be performed, wherein every group generates one data channel. However, a method for the allocation is not presented by Dammann, A. and Raulefs, R. and Kaiser, S., “Beamforming in combination with space-time diversity for broadband OFDM systems”, Communications, 2002. ICC 2002. IEEE International Conference on, pages 165-171, 2002. Further, it is assumed that the antenna processor provides the directions into which the beams are to be formed. Methods for determining the beamforming vectors are not discussed. In Morelos-Zaragoza, R. H. and Ghavami, M., “Combined beamforming and space-time block coding with a sparse array antenna”, Wireless Personal Multimedia Communications, 2002. The 5th International Symposium on, pages 432-434 vol. 2, 2002, beamforming is also considered in the context of STC. The research focus lies on the influence of a correlation between different antenna beams on the performance of the system. Methods for determining suitable beamforming vectors are not considered.
Heath, R. W., Jr. and Paulraj, A., “Multiple antenna arrays for transmitter diversity and space-time coding”, Communications, 1999. ICC '99. 1999 IEEE International Conference on, pages 36-40 vol. 1., 1999 examine what gains may be obtained with different transmitting side diversity technologies in combination with beamforming, and what effect beamforming vectors deviating from the optimum have. The considerations are limited to a system having several antenna groups at the transmitter and one antenna at the receiver (MISO) and only apply under the assumption that only one propagation path exists between one antenna group and the receiver. Further, the research relates to a single user, wherein it is noted that in a multi-user system beamforming is not only to be used for maximizing the received power for the desired user, but at the same time for reducing interference for other users. This principle is also described by Wu, Sau-Hsuan and Chiu, Lin-Kai and Lin, Ko-Yen and Chung, Shyh-Jong, “Planar arrays hybrid beamforming for SDMA in millimeter wave applications” Personal, Indoor and Mobile Radio Communications, 2008. PIMRC 2008. IEEE 19th International Symposium on, pages 1-6, 2008; Wu, Sau-Hsuan and Lin, Ko-Yen and Chiu, Lin-Kai, “Hybrid beamforming using convex optimization for SDMA in millimeter wave radio”, Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on, pages 823-827, 2009; and Smolders, A. B. and Kant, G. W., “THousand Element Array (THEA)” Antennas and Propagation Society International Symposium, 2000. IEEE, pages 162-165 vol. 1, 2000, where hybrid beamforming is considered. It is to be noted that the term “hybrid” refers to the combination of beamforming in the baseband and in the RF-range. The approach does include a transceiver architecture having several parallel transmitting and receiving branches in the digital baseband, however, no MIMO signal processing but beamforming signal processing is performed on the branches. Hence, the same are no hybrid methods in the sense of the above definition.
Thus, there is a need for methods for determining suitable beamforming parameters in a wireless communications system or network including beamforming systems.